In the wave of the AI era, how can individuals ride the crest? On February 18, 2025, coinciding with the traditional Chinese Rain Water solar term, the world's first mass-produced Cybercab, a vehicle without a steering wheel, pedals, dashboard, or rearview mirrors, officially rolled off the production line. Days later, Toyota announced the launch of its first car with Level 4 fully autonomous driving capability, although it still retains a steering wheel.
This series of news garnered widespread attention. Industry experts widely interpreted the market impact of the Cybercab: it signifies a substantive breakthrough in integrated hardware and software AI products. Utilizing an Unboxed modular integrated casting process, its production efficiency is extremely high, reportedly producing a vehicle in under 10 seconds. The manufacturing cost per vehicle is reduced to one-eighth of traditional models. The safety of the Full Self-Driving (FSD) system is claimed to surpass human driving capability by tenfold. The end-to-end data-driven autonomous driving technology pathway has been validated and is rapidly optimizing.
However, public discourse has focused more intently on deconstructing the implications of the "fully unmanned" aspect of the smart driving era and its profound impact on related professions. The term Cybercab combines "Cyber" and "Cab," with Cab meaning taxi or cockpit. How to accurately translate "Cyber" to convey its meaning? Its essence points to the manifestation of digitalization and intelligence in a holographic space—for instance, a cockpit achieving holographic intelligent interaction can be termed a Cybercab. This reveals a significant shift: the agent controlling and operating the holographic space is transitioning from humans to intelligent systems. This is the core logic conveyed by "Cyber." Based on this, translating Cybercab as "Unmanned Intelligent Vehicle" might be more appropriate, signifying that the role of the driver is being replaced by an intelligent agent.
Transforming a biological driver into an intelligent agent is merely the beginning of the "de-humanization" trend, far from its conclusion. The first impact wave of Cybercab directly targets the taxi and ride-hailing driver groups encompassed by "Cab." Subsequent waves can be clearly extrapolated: The second wave, if all Tesla vehicles are equipped with FSD, personal driving becomes unnecessary; if all operational taxis adopt FSD, driver positions in public transport vanish; if private cars can be converted into Cybercabs, the act of human driving itself may become history. The third wave will impact various personnel currently engaged in automotive operation management and services, such as managers of taxi and ride-hailing companies, leaders of platform enterprises, employees in the industrial chain like 4S shop workers, gas station attendants, charging station service personnel, traditional traffic inspectors, and even insurance professionals based on driver risk assessment. A clear signal is that wireless charging technology has received regulatory approval for unmanned automatic recharging of Cybercabs.
This extrapolation outlines a "de-humanization" closed loop, from individual drivers to the entire industrial and service chain, a process approaching at an unexpectedly rapid pace. A fundamental question arises: Can we resist this AI-driven trend of "de-humanization"? Looking back at history, the replacement of horse carriages and roads by cars and highways offers two insights. First, the jobs of coachmen, despite any resistance, were ultimately replaced by car drivers. Those who transitioned earliest from coachmen to drivers became beneficiaries of the trend, while the last to adapt became passive laggards. Second, regulations in some areas once required a person with a red flag to precede cars and strictly limited speed. This restrictive entry management caused regions that invented the car early to lose competitiveness to areas that liberalized reasonable access sooner. This profoundly reveals that personal choice and societal response are crucial when facing historic technological and economic transformations.
The mass production of Cybercab confirms a conclusion: driven by data-driven intelligent computation, the emergence of end-to-end intelligence, and the full integration of in-cabin intelligence with external physical intelligence, has not only proven feasible in testing but has also outperformed other autonomous driving technology paths. In the purely data-driven Cybercab system, the value of data exhibits three clear levels.
The first level consists of historical data accumulated from human driving, particularly represented by driving data from groups like Tesla owners. The more comprehensive, extensive, and scenario-specific this data is, the higher its training value, forming the foundation of intelligence. The second level refers to operational data accumulated before achieving Level 4 autonomy, through designs incorporating safety drivers and allowing manual intervention, along with the corresponding training processes. The third level comprises multimodal intelligent data accumulated during the trial production and operation phases of fully unmanned intelligent vehicles. It is the continuous accumulation and iteration of these three levels of data that ultimately enabled the mass production of the current Cybercab and the complete removal of human-centric design elements like steering wheels, pedals, dashboards, and rearview mirrors.
Regarding first-level data accumulation, we must avoid the misconception that full autonomy has a long development path or may not succeed, leading to indifference. The core recommendation today is: integrate the data generated from your own driving into the development of autonomous driving companies, similar to an "equity investment," allowing this dynamic private data to become part of one's personal intelligent asset portfolio in the AI era. Entering the second level, individuals can gradually transition into roles within Cybercab-related enterprises, such as safety operators, maintenance personnel, data entry and analysts, or specific scenario data simulation and testers.
The logic of the first level is to open-source personally accumulated data to the autonomous driving era, converting it into equity assets with long-term return potential when its value is most prominent. The logic of the second level is to transform current labor into employment in new AI-era positions, becoming workers who earn increased or even超额 labor compensation, rather than being passively淘汰 by the labor market.
Converting past driving data into equity in AI assets is both an inherent necessity for AI companies' development and an awakening of drivers' awareness of their own data assets. Without such dynamic local data, AI companies would struggle to conduct effective first-level intelligent training and achieve technological application and expansion. The value of data provided by drivers at this stage would be maximized, allowing for valuation at a premium and investment as equity in AI companies. If efforts to invest first-level data as equity are coordinated with seeking new employment in the second level, individuals can achieve a proactive and positive transformation towards the AI trend from both asset and employment dimensions.
Effectively converting historical data into equity in AI companies requires innovative financial service support, covering asset rights confirmation, access, and balance sheet inclusion. For rights confirmation, models like GitHub, which significantly increased its own equity value by hosting open-source code assets for free, can be referenced to quickly launch custodial financial services for such data assets. Custodial rights confirmation for these assets can be conducted within existing frameworks like banks, trusts, or asset management institutions. Relevant custodial financial tools are relatively mature; the key is targeting these specific assets for business development. Regarding ownership of driving data, it should be明确归属于 the individual driver, not the platform or management company. This is personal data, not corporate or public data; the driver is the rightful owner. Valuation after custodianship and specific operations for investing in AI companies can be supported by professional service institutions like securities investment firms.
A key concept here is the "Intelligent Agent Singularity." The singularity refers to the moment when the maturity of intelligent driving systems like FSD surpasses human driving and its cost is significantly lower. The core value of past driver data lies in its use during the pre-training phase before the singularity, hence its value can be discovered and command a valuation premium. However, once the singularity passes, the value of original historical data will rapidly衰减, replaced by real-time multimodal data generated by fully unmanned intelligent vehicles becoming the new, high-value data source.
Currently, our automotive intelligence进程 remains pre-singularity, which is precisely the optimal scenario and time-sensitive window for implementing the first and second-level logic. Simultaneously, as Cybercab deployment accelerates, the aforementioned logic is entering a precious implementation window.
The emergence of Cybercabs, or fully unmanned intelligent vehicles, also opens new opportunities for personal asset and investment management. The most immediate is the vehicle asset itself. Upon achieving full autonomy, cars are expected to transition from household consumption assets to operational assets. Beyond personal use, they can be leased for operation 24/7, generating passive asset income for households with considerable potential. It's conceivable that car owners may wish to install FSD modules quickly, and car buyers might consider the vehicle's potential to join mobility rental networks. Deeper asset management could also come from leasing in-vehicle computing network services, turning consumption expenditure into operational asset income. These two areas may become mainstream asset allocation directions for households—AI assets. Starting from this, some trends are predictable: the price increase of expensive parking spaces may stall, as future fully unmanned vehicles could operate almost continuously, reducing demand for parking; charging piles will face迭代升级 with the advent of wireless inductive charging. If households also possess green power facilities like solar panels to power these vehicles, household energy assets can be further optimized and monetized.
The tilt of household asset allocation towards the AI era and AI assets will become a trend catalyzed by the Cybercab's impact. Currently, private equity markets in the US and China are竞相追逐 AI assets; star companies in stock markets are almost all in the AI field; even ETFs related to AI have seen significant gains. Among quantitative funds, those using AI algorithms often achieve超额 returns. Macro-economically, monetary liquidity is directed towards emerging strategic industries like AI, and fiscal policy also favors the AI industry. Globally, pension funds have become important sources of capital for AI companies; personal pensions can only effectively improve the pension replacement rate at retirement by allocating to AI stocks, ETFs, and other assets. In this AI wave, our core advice for household asset allocation is: invest in AI assets through diversified means; face AI, and spring blossoms.
Once fully unmanned intelligent driving takes root, the property insurance business based on human drivers will undergo profound changes. The recent halving of traditional car insurance prices reflects this trend. The unmanned era naturally negates the need for insurance models centered on driver risk. The new requirement for insurance regarding fully unmanned vehicles is to provide humans with a repeatedly verified transportation tool far safer than human driving. The design of such new insurance products, whether for safety testing or claim analysis, should reflect a high responsibility for life. The insurance model will likely be producer-led, focusing on ensuring the vehicle's intrinsic safety—the safer the vehicle, the lower the premium, increasing passenger认可 of this safety model. Interestingly, personal financial支出 previously used for car insurance might transform into investments in new AI insurance companies or related businesses, yielding returns from AI-driven insurance.
AI companies, especially leading ones, are increasingly making the full protection of individual interests a conscious mission. This behavioral model恰好 provides direction for individual choices. This is主要体现在 three levels. First, mission-driven leading AI companies typically offer salaries above social and industry averages, allowing employees to share in corporate growth, aiming naturally to attract and retain top talent. This protective diffusion effect appears not only in employees' high-level contributions and benefits but may also extend to their immediate families. These companies also implement such practices based on ESG (Environmental, Social, and Governance)社会责任 and governance理念, disclosing them transparently to the public. Of course, such practices by leading companies gradually diffuse, influencing other companies and institutions across the industry and society. Second, the widespread implementation of employee stock ownership plans allows staff to gain capital returns from corporate growth. This mechanism not only enables普通员工 to participate in equity plans but also motivates积极性 and creativity through stock options. A notable phenomenon is that after receiving substantial labor skill returns and equity gains, management at these leading companies often becomes the main force behind new ventures, and the startups they found frequently adopt the employee待遇 models of leading companies as standard modules. Third, this is体现为 two aspects: firstly, companies provide high compensation upon business exit or team dissolution, including not only statutory severance but also supplementary compensation for social security, etc.; secondly, entrepreneurs at leading companies tend to allocate capital to social welfare and保障 expenditures, supporting social development while enjoying benefits like tax deductions. A典型 example is the $6 billion endowment fund established by Dell founders Michael and Susan Dell at the end of 2025, aimed at supporting the development of millions of children across the US. Another is the global discussion and practice of "basic income" plans, intended to provide new forms of income supplementation for ordinary people. These corporate-level actions provide direct or indirect financial support for individuals and children, also earning companies social reputation beyond commercial advertising.
The inherent logic of the social security system is to provide basic protection for workers against risks like labor mobility, career changes, and technological shocks. Under contribution pooling mechanisms, whether the first pillar (basic pension insurance), second pillar (enterprise and occupational annuities), or third pillar (personal pensions), the foundation lies in wages and salaries. The more numerous and stronger AI innovation companies are, the more substantial the social security funds they contribute and accumulate, and the more social problems they can help solve. The second pillar's enterprise annuities represent companies sharing operational profits with employees in a capital-sharing form. The development of the third pillar benefits not only from increased disposable income of employees at AI companies leading to higher contribution cash flows but also from employees directly sharing in corporate capital returns through stock plans. If leading companies can further inject funds into the social security system or establish new保障 accounts, it would undoubtedly have a profound positive impact on labor mobility and保障 levels across society, with workers as the primary beneficiaries.
The mass production of the Cybercab marks a solid step forward in the AI era. If a speck of dust from the era falls on an individual's head, it becomes a mountain; what we need to do is use individual rationality and societal synergy to transform that speck of dust into fertile ground for cultivating new mechanisms and logics, unleashing each individual's innovative nature, effectively化解转型 costs, and ultimately merging into the era's relentless torrent. If时代 snowflakes falling on everyone's head are inevitable, then we should strive to turn these snowflakes into spring rain nourishing the heart and spring mud nurturing the future, cherishing everyone's rational choices, and helping everyone gain positive returns in the wave of the AI era.
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